Compute on compressed data, skip the tax
An ML pipeline pays to decompress the same columns thousands of times. AT-1 stores columns as block-compressed segments with a per-block zone map, then extracts features — counts, sums, means, min/max, and range-filtered aggregates — touching only the blocks whose zone map overlaps the predicate. Results are byte-identical to a full scan; a selective query reads a fraction of the bytes.
- 128×
- fewer bytes read on a clustered predicate (touched 1/128 blocks)
- byte-identical
- results equal a full decompress-and-scan, exactly
- 0 bytes
- decoded for summary features — computed from zone maps alone
- SHA-checked
- every block verified on decode; tamper refused
Extract features, read only what you need
at1 features build ticks.csv -o ticks.at1feat at1 features extract ticks.at1feat --where ts:1700262278:1700265482 # WHERE ts in [...] (touched 1/128 blocks, read 4,559 of # 585,272 bytes = 128.4x less): # qty: count=1601 sum=397013 mean=247.98 min=1 max=500 at1 features extract ticks.at1feat # summary: zone-maps only, 0 bytes decoded
Zone-map pushdown
Blocks whose min/max can’t overlap the filter are skipped without a single byte read — the reduction scales with how clustered your predicate is.
Exact, and verified
Features are byte-identical to a full scan; every decoded block is SHA-checked against the manifest, so tampering is refused.
Honest boundary
The reduction depends on physical clustering. A random-scattered predicate degrades toward a full scan — the tool reports the exact bytes touched so you always see where you sit.
Billed per extraction — first 10,000/month free. See pricing.